The University of Southampton
University of Southampton Institutional Repository

An evolutionary cellular program on the solution of the travelling salesman problem

An evolutionary cellular program on the solution of the travelling salesman problem
An evolutionary cellular program on the solution of the travelling salesman problem
An evolutionary algorithm is described for solving instances of the Travelling Salesman Problem (TSP). The key point of the algorithm is the distribution of the population over a grid, being in that sense a sort of cellular automata, but having the rules of change an iteration of a genetic algorithm each time. This genetic algorithm operating in miniature just takes account of a subset of the whole population, using for that a given neighbourhood relation, among several defined. This work compares the behaviour of the algorithm against a genetic algorithm, for different program's parameters. The set of benchmark instances was taken from the worldwide known collection TSPLIB.
959-02-0241-1
236-241
Academia
Moreno, José A.
d098003b-0658-44df-afc8-ed42d75b5f8d
Egea, Adriana G.
37ee0dec-a07f-4177-b291-96037fe48e14
Mühlenbein, H.
Ochoa, A.
Moreno, José A.
d098003b-0658-44df-afc8-ed42d75b5f8d
Egea, Adriana G.
37ee0dec-a07f-4177-b291-96037fe48e14
Mühlenbein, H.
Ochoa, A.

Moreno, José A. and Egea, Adriana G. (1999) An evolutionary cellular program on the solution of the travelling salesman problem. Mühlenbein, H. and Ochoa, A. (eds.) In Proceedings of the Second International Symposium on Artificial Intelligence - Adaptive Systems, ISAS’99. Academia. pp. 236-241 .

Record type: Conference or Workshop Item (Paper)

Abstract

An evolutionary algorithm is described for solving instances of the Travelling Salesman Problem (TSP). The key point of the algorithm is the distribution of the population over a grid, being in that sense a sort of cellular automata, but having the rules of change an iteration of a genetic algorithm each time. This genetic algorithm operating in miniature just takes account of a subset of the whole population, using for that a given neighbourhood relation, among several defined. This work compares the behaviour of the algorithm against a genetic algorithm, for different program's parameters. The set of benchmark instances was taken from the worldwide known collection TSPLIB.

Text
ISAS99 Evolutionary Cellular Program - Accepted Manuscript
Download (2MB)

More information

Published date: March 1999
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 349759
URI: http://eprints.soton.ac.uk/id/eprint/349759
ISBN: 959-02-0241-1
PURE UUID: f9b1efa1-43b9-46b8-b11a-b7fed03c7cdb
ORCID for Adriana G. Egea: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 12 Mar 2013 15:26
Last modified: 07 Jul 2020 00:36

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×